This study examined the classification accuracy of the activPAL, including total time spent sedentary and total number of breaks in sedentary behavior (SB) in 4- to 6-year-old children. Forty children aged 4–6 years (5.3 ± 1.0 years) completed a ~150-min laboratory protocol involving sedentary, light, and moderate- to vigorous-intensity activities. Posture was coded as sit/lie, stand, walk, or other using direct observation. Posture was classified using the activPAL software. Classification accuracy was evaluated using sensitivity, specificity and area under the receiver operating characteristic curve (ROC-AUC). Time spent in each posture and total number of breaks in SB were compared using paired sample t-tests. The activPAL showed good classification accuracy for sitting (ROC-AUC = 0.84) and fair classification accuracy for standing and walking (0.76 and 0.73, respectively). Time spent in sit/lie and stand was overestimated by 5.9% (95% CI = 0.6−11.1%) and 14.8% (11.6−17.9%), respectively; walking was underestimated by 10.0% (−12.9−7.0%). Total number of breaks in SB were significantly overestimated (55 ± 27 over the course of the protocol; p < .01). The activPAL performed well when classifying postures in young children. However, the activPAL has difficulty classifying other postures, such as kneeling. In addition, when predicting time spent in different postures and total number of breaks in SB the activPAL appeared not to be accurate.

cardiometabolic risk in children may be a result of measuring sedentary time using accelerometers that are unable to detect posturalallocation. Therefore, standing time could be misclassified as sitting. 8 , 14 , 15 , 17 , 18 This is problematic as it may lead to overestimations of sedentary time and

Scott E. Crouter, Paul R. Hibbing and Samuel R. LaMunion

were based on direct observation of the activity during minutes 5–7 of each activity. Postureallocation was a secondary focus within the larger study, thus we chose to only collect posture data for a short period when the activity was most stable. Data from minutes 5–7 of each activity were included

controlled for during the laboratory conditions to mimic “real-world” conditions. Although the accelerometer is an acceptable measure of physical activity and SED, it does not measure changes in posturalallocation. Finally, menstrual cycle was not controlled in this trial. This may affect the results of the

influence healthy aging. 29 , 30 New monitoring systems for determining posturalallocation (ie, sitting, reclining, lying, and standing) as the ActivPAL or the Intelligent Device for Energy Expenditure and Activity (IDEEA) could provide more precise estimates of SB, and they can be useful for validating